Sf Tensor Member of Technical Staff to own the post-training pipeline end-to-end including data curation, SFT, preference optimization, RL, evals, distillation, and deployment. Work on modeling side of enterprise offering for specialist models.
Responsibilities
We build the fastest GPU compiler in the world. Most compilers have to preserve correctness at every transform, constraining how far they can search, while we prove correctness at the end instead, allowing us to search a far wider space, with agents, with RL, with anything that works and still guarantee the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels.
Speed at the kernel layer is only worth what we do with it though and our enterprise offering promises we'll turn a customer's dataset into a specialist model in days. To do that we run SFT, RL, DPO, design evals and distillation down to smaller and edge-deployable models. We're hiring a Member of Technical Staff to own the modeling side of that end to end.
The infrastructure you'll be working on top of is unusually strong, which is the only reason we're able to train models so fast. Every experiment goes through Model Foundry, which let's you run experiments on the best hardware, look at overviews or dive deep into any detail all in a versioned and reproducible manner, making it trivial to reuse and modify recipes. Underneath that is the most powerful engine you could ask for, which has been used to post-train multi-100B language parameter models on TPU, post-train robotics models on Trainium and pre-train AlphaFold v3 on AMD.
You'll own the post-training pipeline end-to-end: data curation, SFT, preference optimization, RL, evals, distillation and finally deployment
You'll design reward functions and RL looks along customer domain experts, who know the task inside-out but not our training stack
You'll build an eval harness trustworthy enough to make a ship/no-ship call within a short window, especially where the target is subjective taste rather than a scored benchmark
You'll structure and generate datasets, including synthetic data pipelines, from whatever the customer actually has
You'll distill specialist models down into smaller models
You'll drive the time-to-model, which means finding what's actually on the critical path and removing it, run after run
You'll embed with customers as a forward-deployed researcher, then hand the pipeline over cleanly when their team is ready to take over
Qualification
Someone who's comfortable owning dataSomeone proficient in PyTorch or JAXSomeone with experience in distillation
Required
Someone who's shipped post-trained models into production and can talk honestly about the tradeoffs
Someone with hands-on depth across SFT and RL (DPO, GRPO, PPO or similar)
Someone who can judge evaluation honestly: what to measure, what a result means and when a number is lying to you
